DataBloomAI is at play on a global stage, and local developers are stealing the show. Grassroot communities are essential to driving AI innovation, according to Kate Kallot, head of emerging areas at NVIDIA. On its opening day, Kallot gave a keynote speech at the largest AI Expo Africa to date, addressing a virtual crowd of Read article >
The post The Bright Continent: AI Fueling a Technological Revolution in Africa appeared first on The Official NVIDIA Blog.
The transportation industry is adding more torque toward realizing autonomy, electrification and sustainability. That was a key takeaway from Germany’s premier auto show, IAA Mobility 2021 (Internationale Automobil-Ausstellung), which took place this week in Munich. The event brought together leading automakers, as well as execs at companies that deliver mobility solutions spanning from electric vehicles Read article >
The post Autonomy, Electrification, Sustainability Take Center Stage at Germany’s IAA Auto Show appeared first on The Official NVIDIA Blog.
Running PyCarert on GPU not only streamline model building but offsets the time cost.
PyCaret is a low-code Python machine learning library based on the popular Caret library for R. It automates the data science process from data preprocessing to insights, such that short lines of code can accomplish each step with minimal manual effort. In addition, the ability to compare and tune many models with simple commands streamlines efficiency and productivity with less time spent in the weeds of creating useful models.
The PyCaret team added NVIDIA GPU support in version 2.2, including all the latest and greatest from RAPIDS. With GPU acceleration, PyCaret modeling times can be between 2 and 200 times faster depending on the workload.
This post will go over how to use PyCaret on GPUs to save both development and computation costs by an order of magnitude.
All benchmarks were run with nearly identical code on a machine with a 32-core CPU and four NVIDIA Tesla T4s. For simplicity, GPU code was written to run on a single GPU.
Using PyCaret is as simple as importing the library and executing a set-up statement. The setup() function creates the environment and offers a host of pre-processing features all in one go.
from pycaret.regression import *
exp_reg = setup(data = df, target = ‘Year’, session_id = 123, normalize = True)After a simple setup, a data scientist can develop the rest of their pipeline, including data preprocessing/preparation, model training, ensembling, analysis, and deployment. After the data is prepared, a great place to start is by comparing models.
True to PyCaret’s ethos of simplicity, we can compare a host of standard models to see which are best for our data with a single line of code. The compare_models command trains all the models in PyCaret’s model library using default hyperparameters and evaluates performance metrics using cross-validation. A data scientist can then select the models they’d like to use, tune, and ensemble based on this info.
top3 = compare_models(exclude = [‘ransac’], n_select=3)
**Models are sorted best to worst, and PyCaret highlights the top results in each metric category for ease of use.
PyCaret is a great tool for any data scientist to have in their arsenal, as it streamlines model building and makes running many models easy. PyCaret can be made even better with GPUs. Since PyCaret does so much work behind the scenes, seemingly simple commands can take a long time. For example, we ran the commands preceding on a dataset with roughly half a million instances and over 90 attributes (UC Irvine’s Year Prediction MSD dataset). On the CPU, it took over 3 hours. On a GPU, it took less than half that.
In the past, using PyCaret on a GPU would have required many manual coding, but thankfully, the PyCaret team has integrated the RAPIDS machine learning library (cuML), meaning you can use the same simple API that makes PyCaret so effective while also using the computational ability of your GPU.
Running PyCaret on a GPU tends to be much faster-meaning you can make full use of everything PyCaret has to offer without balancing time costs. Using the same dataset just mentioned, we tested PyCaret ML functionality on both a CPU and a GPU, including comparing, creating, tuning, and ensembling models. Performing the switch to GPU is simple; we set use_gpu to True in the setup function:
exp_reg = setup(data = df, target = ‘Year’, session_id = 123, normalize = True, use_gpu = True)With PyCaret set to run on GPU, it uses cuML to train all of the following models:
Running the same compare_models code solely on GPU was over 2.5 times as fast.
The impact was even greater on a model-by-model basis with popular but computationally expensive models. The K Neighbors Regressor, for example, was 265 times as fast on GPU.
The simplicity of PyCaret’s API frees up time that would otherwise be spent coding so data scientists can do more experiments and fine-tune their experiments. When paired with GPUs, this impact is even greater, as the computation costs of taking full advantage of PyCaret’s suite of evaluation and comparison tools are significantly lower.
Extensive comparing and evaluating models can help improve the quality of your results, and doing so efficiently is exactly what PyCaret is for. PyCaret on GPU offsets the time costs that go along with so much processing.
The goal of RAPIDS is to accelerate your data science, and PyCaret is among a growing list of libraries whose compatibility with the RAPIDS suite can help bring a new layer of efficiency to your machine learning pursuits.
**Code used for this notebook can be found here.
Combining mentoring, socializing, and specialized training proved key for the virtual 2021 KISTI GPU Hackathon.
Due to the coronavirus, the 2021 Korea Institute of Science and Technology Information (KISTI) GPU Hackathon was held virtually, under the guidance of expert mentors from KISTI, NVIDIA, and the OpenACC Organization. With the goal of inspiring possibilities for scientists to accelerate their AI research or HPC codes, the hackathon provided opportunities for solving research problems and expanding expertise using NVIDIA GPU parallel computing technology.
Known for being a face-to-face event, a virtual hackathon poses its own challenges for both attendees and hosts. The new format also required juggling a diversity of teams—composed of three HPC and AI teams, four higher education and research teams, and two industry teams.
The event team found the following recipe helped create a meaningful and successful experience for the participants:
Based on their expertise in specific domains or programming languages, dedicated mentors were paired with teams for guidance in setting goals, and considering different approaches. The mentors collaboratively worked to solve problems and troubleshoot obstacles the teams encountered. Daily mentor sync-up calls each day kept everyone focused and working toward the best strategy for meeting their goals.
Everyone knows that all work and no play can actually hinder a team’s productivity. The hackathon provided a TGIF social hour session for participants and mentors. Using the Metaverse Gather Town Space, mentors and teams shared experiences, recharged their batteries, and developed connections that helped them continue forward for the duration of the event.
Another important ingredient to success was making specialized training and resources available to attendees. For example, an NVIDIA Deep Learning Institute (DLI) workshop covering CUDA C/C++ topics was presented by a DLI ambassador and mentor. Other mentors provided team-dedicated tech sessions focused on TRT and NVIDIA Triton, OpenACC, and Nsight systems for profiling, parallel computing, and optimization.
The PasCal team from Yonsei University is developing a thermal fluid solver that efficiently calculates the thermal motion of turbulence. At this hackathon, the team converted existing code based on CPUs to a multi-GPU environment through OpenACC and cuFFT Library. This resulted in accelerating the calculations by 4.84 times RHS (right-hand side, fraction step) of one of most time consuming subroutines.
The Amore Opt team from AmorePacific cosmetics company worked on GPU optimization of DeepLabV3 + segmentation model. By applying what they learned about the TensorRT Inference optimizer and NVIDIA Triton inference server, they improved inference speed making it 26 times faster. They did this while maintaining the accuracy in their AI models to detect skin problems for their future large-scale customer service.
The TFC team from Seoul National University joined a project to accelerate a CPU-based Fortran in-house fluid calculation code. By using NVIDIA GPUs at KISTI, the team accelerated the time-consuming Tri-Diagonal Matrix Algorithm (TDMA) for thermal solver and momentum solver and Fast Fourier Transform (FFT) for pressure solver calculations. They achieved a speed 11.15 times faster on a single V100 GPU.
NVIDIA Inception member Nota and HangYang University teamed-up to optimize the Nota Model compression engine by leveraging the Tensor Core in NVIDIA GPUs for INT4 quantization. Named NOTA-HYU, the team learned to use NVIDIA profiling tools Nsight system and Nsight Compute. They then applied NVIDIA library CUTLASS to achieve an overall speed 1.85 times faster for their residual block with CUDA optimization.
For more information on GPU Hackathons and future events, visit https://www.gpuhackathons.org/
Also don’t miss out on the OpenACC Summit 2021 scheduled from September 14-15, 2021.
Hello,
I just started using Tensorflow and Keras not long along ago, and I really like the field of deep learning. Right now I am doing it as more of a hobby than anything, and I recently learned about the concepts of transfer learning and fine-tuning. I tried to apply them to a dataset of microscopic images using the tutorial here: https://www.tensorflow.org/tutorials/images/transfer_learning.
I am using ResNet50 with the ImageNet weights, but am far from getting good results. I think it might be because of the learning rate OR because of the activation function in my last layer OR because of the fact that I use the Adam optimizer and not SGD.
Would someone be willing to look into my code to see what’s wrong? I have uploaded it as a pdf here: https://www.mediafire.com/file/vteka9uje8lthnb/NNonNema.pdf/file
Please note that the document is long because I printed model.summary() at some point, which showed all the layers!
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I’m looking for some advanced TensorFlow Udemy courses if there are any.
I have a good track record in Statistics and Machine Learning, so I’m mostly looking for courses that focus on the coding itself.
I really appreciate any help you can provide.
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Learn how AI is transforming the retail industry through enabling intelligent stores, omnichannel management, and automated supply chains.
Global retailers and suppliers are faced with navigating rapidly changing consumer demand, behavior, and expectations. These changes are driving uncertainty in forecasting and putting pressure on global supply chains as well as omni-channel and store operations. Real-time agility is required to navigate the rapidly evolving challenges for the retail industry.
Artificial intelligence offers a powerful solution for retailers to rapidly and more accurately forecast daily demand and automate supply chain logistics. This has a major impact on in-store supply and last-mile delivery cost challenges, while meeting consumer expectations on delivery timelines. For online shoppers, AI is helping create personalized shopping journeys and product recommendations. In-store, retailers are using AI to reduce shrinkage and stockout, while creating frictionless experiences and ensuring the health and safety of employees and shoppers.
The financial opportunity is significant across the $26 trillion retail industry, which historically has averaged a 2% net profit margin. An estimated 3X increase in profit margin with AI-enabled solutions would lead to over a $1 trillion increase in annual revenue for retailers, according to analysis by the McKinsey Global Institute. From customer engagement, to operational agility, to seamless omnichannel management, AI at the edge is transforming retail.
Retailers are leveraging AI at the edge to analyze data from in-store cameras and sensors, and create intelligent stores. One application alerts store associates when shelf inventory levels are low, reducing the impact of stockout. Another is decreasing shrinkage—the loss of inventory from theft, errors, fraud, waste, and damage—which costs the industry an estimated $100 billion per year globally. AI helps retailers protect their assets with store analytics that monitor points-of-sale and floor merchandise to prevent ticket switching, misscans, and shoplifting.
Deep North, a computer vision startup, uses intelligent video analytics to power in-store analytics, providing insights to customer traffic and heatmaps, determining dwell times, queue/wait times, conversion metrics, demographics, and more. These insights help retailers improve inventory planning, store layout and merchandising, and improve the customer experience and conversion.
Frictionless store concepts are being tested where shoppers can skip the checkout lines entirely and be automatically billed for their purchases. AiFi is a leader in these AI-enabled autonomous stores from nano stores to full-sized grocery chains. These “grab-and-go” stores are rising in popularity and locations are projected to expand 4X in the next 3 years.
Personalized experiences are critical in ecommerce, which can account for as much as 30% of revenue for the world’s largest retailers. Underlying ecommerce are sophisticated recommender systems (RecSys). GPU-powered machine learning and deep learning enable recommenders by learning how customers shop, personalizing their shopping experience, and finding related items that shoppers are most likely to purchase. NVIDIA has deep domain expertise with our data science teams winning three global RecSys competitions in 5 months.
To support the online personalization and recommendations, global retailers are using AI to automatically generate metadata for new items listed in their vast digital catalogs. With up to millions of new products to onboard, comprehensively and accurately labeling and describing every product is a daunting task. AI quickly produces accurate, comprehensive, and engaging product content that a RecSys engine uses to provide personalized recommendations that attract a shopper’s interest.
Visual search is a key trend for retailers to provide a customer-centric experience with product search and discovery. Clarifai, a startup and NVIDIA Inception member, uses computer vision and AI to deliver more relevant search results and hyperpersonalized product recommendations quickly. With “snap-and-search” capabilities, shoppers are able to take a photo of a product they’re interested in with their mobile device and automatically have the photo matched to a product catalog. Related products to complete their desired outfit, room, or other look are also shared.
Smart virtual assistants, used by hundreds of millions of users each month, are also driving growth in ecommerce. Retailers are looking to improve the digital customer experience by introducing voice ordering to replace text-based searches with voice commands. With voice ordering, shoppers can easily search for items, ask for product information, and place online orders using smart speakers or other intelligent mobile devices.
AI empowers retailers to create more resilient supply chains that respond quickly to changing consumer demand and effectively manage inventory distribution.
Walmart, for example, uses AI to run accurate daily forecasts of millions of item-to-store combinations across thousands of stores in the United States. Built on open-source data processing and machine learning libraries, this AI-powered platform enabled Walmart to get the right products to the right stores more efficiently, react in real time to shopper trends, and realize inventory cost savings at scale.
In smart warehouses, AI improves operational efficiency and throughput with customer orders automatically picked, packed, and shipped by robots. These robots use edge computing and intelligent video analytics to identify, position, and sort packages while adjusting the speed of conveyor belts to minimize product damage and machine downtime.
KION Group simplifies the deployment and management of AI at the edge— including autonomous forklifts and pick-and-place robotics—across thousands of retail distribution centers.
AI delivers end-to-end visibility that combines data from GPS, weather, traffic, and construction to determine optimal shipping routes. This can significantly reduce overhead costs for last-mile delivery associated with fuel, transportation, and delivery personnel—and can provide more accurate delivery windows that enhance customer service and create more satisfied shoppers.
Billions of connected sensors are coming online in retail stores, city streets, hospitals, warehouses, and more. By deploying and managing scalable, secure AI at the edge, enterprises can turn this data into faster insights and more robust services and applications.
Learn more about NVIDIA retail solutions >>
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BlueField-2 offers protection, while delivering high security, integrity, and reliability for the new hybrid cloud era.
Data Processing Units, or DPUs, are the new foundation for a comprehensive and innovative security offering. The hyperscale giants and telecom providers have adopted this strategy for building and securing highly efficient cloud data centers, and it’s now available for enterprise customers. This strategy has revolutionized the approach to minimize risks and enforce security policies inside the data center.
A DPU is an integrated system on a chip (SOC) that combines high-performance CPU, network interface, and data center function accelerators into a single ASIC. DPUs utilize programmable hardware to offload and accelerate inline security services at line-rate.
The NVIDIA® BlueField®-2 DPU provides the first line of defense against attacks. Internal attacks that try to infiltrate the data center are prevented through an isolated, secure boot process and secure firmware updates. Aimed at accelerating security throughout the data center, DPUs are capable of filtering packets in support of next-generation firewalls and intrusion detection/prevention systems. A DPU can detect, block, and protect sensitive assets and data from threats.
The design offers built-in functional isolation to protect individual workloads and provide flexible control and visibility at the server level, reducing risk, and increasing efficiency. The isolation enables software agents and applications to run securely on the DPU, irrespective of the rest of the system.
Isolated from the host, and leveraging unique hardware capabilities, the DPU can deliver better security by reducing the attack surface and bolstering individual workload isolation—providing additional protection to reduce risks and simplify security management policies.
Furthermore, DPUs offload and accelerate cryptographic operations and offer encryption of data at rest or in motion. Freeing the host CPU to run critical applications, and being isolated from the application domain, keeps the cryptographic keys secure from a potentially compromised host.
The adoption of a cloud compute model calls for intelligent security solutions capable of delivering maximum performance and agility. In the age of hybrid cloud and virtualized computing, security functions have transformed. They are being deployed within every host to provide strict policy enforcement, as well as visibility into possible attacks.
Previously, protection within the host required running a software security solution or VM appliance, which was slow and consumed CPU resources. As new technology and bandwidth requirements increase, host-based protection now requires hardware performance at line-rate.
BlueField-2 DPU can be used as such a device to filter traffic traveling between computers. NVIDIA DPUs employ powerful hardware and software networking components that are programmable, with a configurable set of rules to inspect all traffic traversing the connection at line-rate. When combined with perimeter security solutions, a DPU can extend security to include host-based protection.
The DPU can be used as a platform that sits at the ingress and egress points of each host, adding a new layer where it’s needed most—at the computing edge. Together, they better protect against malicious threats to company assets and resources.
Software-based firewalls also place an additional burden on the CPU, reducing available computing resources for application processing and decreasing the number of VM, applications, and services that can run on a single host. BlueField-2 DPU security platform, with a software security stack, offloads security services from the host, freeing CPU cycles for applications running on host resources.
The complexity of security increases with the use of computing and network virtualization technologies. Micro-segmentation is a network security technique in virtualized environments that logically divides a data center into distinct individual security segments at the workload level. At this low-level, security controls can be defined, and security services delivered for each unique segment. BlueField-2 provides a platform for hosting micro-segmentation and network connection tracking services for flow-based network analytics and application-level security for traffic inside the data center.
The DPU can run a security software stack, leaving no impact on servers or hindering application performance. This hardware-accelerated solution includes security policies and enforcement capabilities at wire speed and is fully isolated from the application workloads themselves.
The decade-old approach of perimeter security defense has reached a tipping point of performance limitations and operational complexities. A new holistic approach to security is needed for enterprises to achieve robust protection. Enterprise data centers are evolving and following the lead of hyperscale and public cloud providers with CDI and should also adopt their similar tactics for network security. BlueField-2 can offer protection for all types of workloads against current and future threats, and can deliver the highest security, integrity, and reliability for the new hybrid cloud era.
As the cybersecurity landscape grows in complexity, security experts continue to turn to NVIDIA BlueField-2 as a platform to provide advanced security features.
Our security experts are here to help answer questions about how BlueField-2 can augment and expand your security solution. If you’re a security architect or product manager at a cybersecurity company, contact us.
Simulations are pervasive in every domain of science and engineering, but they often have constraints such as large computational times, limited compute resources, tedious manual setup efforts, and the need for technical expertise. Neural networks not only accelerate simulations done by traditional solvers, but also simplify simulation setup and solve problems not addressable by traditional … Continued
Simulations are pervasive in every domain of science and engineering, but they often have constraints such as large computational times, limited compute resources, tedious manual setup efforts, and the need for technical expertise. Neural networks not only accelerate simulations done by traditional solvers, but also simplify simulation setup and solve problems not addressable by traditional solvers.
NVIDIA SimNet is a physics-informed neural network (PINN) toolkit for engineers, scientists, students, and researchers who are getting started with AI-driven physics simulations. You may also be looking to leverage a powerful, existing framework to implement your domain knowledge and solve complex nonlinear physics problems with real-world applications.
A success story of SimNet’s application today is in the use of hybrid PINNs for digital twins in prognosis and health management. This effort was led by Prof. Felipe Viana, an assistant professor at the University of Central Florida. He leads the group’s research in state-of-the-art probabilistic methods fusing physics-based domain knowledge and multidisciplinary analysis and optimization with applications in design, diagnostics, and prognostics.
Maintenance of engineering assets and industrial equipment (such as aircraft, jet engines, wind turbines, and so on) is critical for safety as well as enhanced profitability in the services and warranties of these assets. Effective preventive maintenance requires the knowledge of the various operating parameters and their impact on the wear and tear of equipment. Simulations, advanced analytics, and deep learning algorithms enable the predictive modeling of complex systems and their operating environment.
Unfortunately, building models that estimate residual useful life for such equipment in large fleets is daunting. This is due to factors such as duty cycle variations, harsh environments, inadequate maintenance, and mass production problems that cause discrepancies between designed and observed component lives.
In this research project, Prof. Viana and his team of researchers built predictive models for fatigue crack growth prognosis on aircraft window panels (Figure 1), where models were trained using historical flight records (origin and destination airports, cruise altitude, and so on) and limited inspection observations (such as the crack length data for only a portion of the fleet, and so on). When the model was built and validated, it was then applied in a fleet of 500 aircraft to analyze the success of the model on larger data sets. Such predictive models are also called digital twin models and they have been increasingly used in prognosis and health management applications of industrial equipment.
Based on literature and freely available data, synthetic data for a representative fleet of 500 narrow-body aircraft was created. The fleet is equally divided into 10 route structures (Figure 2). Each aircraft flies an average of five flights per day.
After four years of operation, the fleet starts being inspected. At this point, consider a case in which after inspecting 25 aircraft, the operator finds that some exhibit fatigue cracks at the corner of a given window. These cracks happen to be larger than anticipated. From a scientific standpoint, this poses the following challenge: if predictions were wrong due to model assumptions, is there a way to correct that?
The business implication is straightforward. When such a discrepancy is observed, operators must decide which aircraft to inspect next.
Assume that you have available the flight data from the fleet for the past four years. The hoop stresses that govern the fatigue crack growth are a function of the aircraft cabin pressure differential, which is a function of cruise altitude. The purely physics-based model assumes the local geometry correction factor F = 1.122. In reality, that is ultimately a function of the crack length. The digital twin for this component must be predictive and aircraft-specific, in addition to being computationally efficient.
There are two main challenges. First, the data is highly unbalanced. For the aircraft fleet that was being analyzed, there were only 182,500 input points and only 25 output points. Building purely machine learning models under such circumstance is extremely hard.
Second, while the conventional physics-based models maybe accurate, they often entail engineering assumptions regarding loading conditions. Considering that the sample size for this problem includes a fleet of 500 aircraft, these simulations must be done a few million times.
To overcome these challenges, Prof. Viana and team developed a novel hybrid physics-informed neural network model. Figure 3 shows where they performed cumulative damage accumulation based on recurrent neural networks merging physics-informed and data-driven layers.
Full-fledged finite element analysis configured for crack growth simulation is extremely expensive. Therefore, it is simply not feasible for digital twin applications. This is true even if we were to run simulations for hundreds of aircraft many times over, as we optimized inspections and decided how to swap routes for a few aircraft. A parameterized physics-driven AI model is constructed in SimNet that satisfies the governing laws of linear elasticity, as follows:
Here, 
Unlike the traditional data-driven models, no training data is used here. Instead, the loss functions are augmented by the linear elasticity laws, and the required second-order derivatives are computed using automatic differentiation. Initial and boundary conditions are also imposed as soft constraints to fully specify the physical system. Several techniques are used to enhance the accuracy and convergence speed of the model, such as network weight normalization, signed distance loss weighting, differential equation normalization and nondimensionalization, and XLA kernel fusion.
After a single training, this parameterized model provides instantaneous prediction of cyclic stresses for a variety of different loading conditions. This instantaneous prediction is critical in digital-twin applications where real-time predictions are needed. However, traditional solvers can only solve for one configuration at a time. Moreover, data-driven, surrogate-based approaches suffer from interpolation error and the predictions may not satisfy the governing laws.
For this trained parameterized model, several SimNet predictions are validated against a commercial solver, showing a close agreement with a difference of less than 5% in the maximum Von Mises stresses. Training of this model was performed on a single V100 GPU. SimNet also offers scalable performance for multi-GPU and multi-node implementation with TF32 support for accelerated convergence.
In Prof. Viana’s model, engineers and scientists can use physics-informed layers to model well-understood phenomena. This might include mechanical stress calculations to estimate the damage accumulation with Paris law fatigue increment block. Another example would be using data-driven layers to model poorly characterized parts such as corrections in stress intensity due to geometry of crack. This is where hybrid models can help estimate fatigue crack growth on fuselage panels with window cutouts.
In Figure 4, the “before training” curve uses purely physics-based models. These are obtained through numerical integration of Paris law using linear elasticity in the calculation of cyclic stresses:
: number of cycles
and 
: material properties (obtained through coupon tests)
In Figure 4, the “after training” curve uses the hybrid model where the MLP layer in the RNN cell compensates for missing physics (adjusting predictions without violating the physics).
After the hybrid model is trained, we use it to predict crack length histories for the entire fleet of 500 aircraft. The 500 pressure differential time series amount for more than 3.5M data points. The hybrid cumulative damage recurrent neural network, using SimNet for stress calculations, predicts the crack length histories in 5 years. The results are illustrated in Figure 5. This enables the operators to prioritize which aircraft will be brought for inspection or swapping aircraft of different route structures.
Operators could use the hybrid model to analyze the entire fleet. The results dashboard could visualize the most aggressive route structures in terms of cumulative damage rates; number of aircraft at high, medium, or low risk levels; as well which tail numbers are in which buckets (Figure 6).
In terms of actionable outcomes, operators could use the hybrid model to decide which aircraft should be brought for inspection next. They could swap aircraft between routes so that damage accumulation is mitigated while inspections are performed.
This framework handles highly unbalanced datasets formed by few output observations and data lakes containing time series used as inputs. GPU computing enables scaling up computations for fleets of hundreds of aircraft, keeping training under a few hours and inference under a few seconds.
Prof. Viana’s application’s implementation has been done in TensorFlow v2.3 using the Python APIs on various NVIDIA GPUs. Depending on the application and computing needs, you can use high-performance GPU-based clusters, a smaller Linux server with few GPUs, or even the NVIDIA Jetson. For this study, we used a Linux server with two Intel Xeon Processor E5-2683 and two NVIDIA P100 GPUs.
In the future, Prof. Viana and his team plan on expanding the use of SimNet in their hybrid PINNs for cumulative damage by tackling even more complex applications where loading and deformation are highly nonlinear and potentially involve multiphysics. Given the flexibility of the approach, he hopes to expand the applications to other industries besides civil aviation and failure modes, such as corrosion and oxidation.
Prof. Viana elaborated on his experience. “SimNet’s accuracy is comparable to other computational mechanics software. However, its computational efficiency, quick turnaround time, and easy integration with existing machine learning frameworks makes it our choice of toolkit for our simulation needs. With SimNet, we scaled our predictive model to a fleet of 500 aircraft and get predictions in less than 10 seconds. If we were to perform the same computations using high-fidelity finite element models, we could be talking about a few days to a week. As a research institution, we see SimNet as a tool of the future where it unlocks possibilities and enables us to explore modeling approaches that were not possible before.”
For more information, see the following resources:
To ask questions about Prof. Viana’s work and see what others are doing with SimNet toolkit, join the SimNet forum. For more information about SimNet’s features and to download the SimNet toolkit, see NVIDIA SimNet.
